In fitness studios, weight rooms, health centers, physiotherapy practices or rehabilitation institutions, athletes/patients are provided with a large number of training utensils, which, inter alia, can be subdivided into training with dumbbells, barbells, own body weight, stationary machines/equipment, cable machines and cardio-equipment. Due to the low opposing force of the cardio-equipment, endurance training is carried out; by way of example, the pulse is acquired and the calorie consumption is calculated. By contrast, in strength training work is undertaken against higher loads and e.g. dumbbells, barbells, machines, cable machines or the own body weight are used as opposing force. The term strength training can also include bodybuilding, muscle strength training and, in parts, fitness training. The goal of strength training, particularly in the case of fitness-oriented strength training, is to increase the maximum strength and the muscle increase connected therewith. The previous inventions in strength training usually acquire the training data on large, stationary machines, for example by means of cable pull sensors. Other mobile devices measure parameters such as e.g. change in angle, force, speed or power, which are applied for direct assessment or optimization of the performance. In the following text, the previous inventions and options for training data acquisition and the analysis of this training data are explained.
EP 1834583B1 and US 20110207581A1 describe an invention which uses accelerometers to calculate parameters such as e.g. muscular strength, speed, power, height of the jump in the case of countermovement jumps, reactivity, muscular elasticity property or coordination by carrying out test movements in order to acquire directly the training state or the performance level of the athlete and to optimize the training by calculations on the basis of the acceleration values. Here, this involves a limited number of tests, such as e.g. the acquisition of the jump height in the case of a countermovement jump. The training optimizations are based on acceleration data or the aforementioned muscular parameters. A personalized “muscular profile” (US 20110207581A1, page 3, [0043]) is based on strength, power and speed curves. “Personalized power curves” (US 20110207581A1, page 3, [0044]) render it possible to set the training load in order to cause specific adaptations by selected training regions (e.g. muscle hypertrophy). This form of determining the maximum strength (repetition maximum, abbreviated RM) sets the training intensity load parameter, i.e. the training load, as a result of which the training is to be optimized.
U.S. Pat. No. 6,280,361B1 describes an invention which generates tension forces with the desired resistive force in several cables by means of a controlling structure. Using this invention, any form of training exercise can be carried out with a resistive force, even in a gravity-free environment. This invention enables stationary strength training.
WO 9426359 describes an invention, which acquires the movement of a joint by means of an inclination sensor. By means of this invention, it is possible to store individual predetermined rehabilitation programs and to acquire the fulfillment of the rehabilitation program on the basis of angle measurements in the joints. This invention is characterized in that it undertakes calculations by means of an inclination sensor.
U.S. Pat. No. 0,250,286A1 describes an invention for monitoring movements of a subject by means of a multiplicity of sensor elements attached to movable body segments of a subject. By means of this invention, it is possible to register a multiplicity of movements during acute and chronic lifting tasks in order to determine and correct disease of the lumbar vertebral column and repetitive load injuries.
JP 2007209636 describes an invention, which enables an individual undertaking training to acquire measurement variables from a training repetition, such as time or frequency, by means of an accelerometer and to transfer said measurement variables to a computer.
U.S. Pat. No. 6,796,925B2 describes an invention which can measure the movement repetitions of training exercises of an athlete by means of a proximity sensor. By means of this invention, it is possible to acquire the number of movement repetitions in certain exercises.
US 20080090703A1 describes an invention for automatically counting repetitions and orchestrating exercises. This invention enables access to a predetermined training program from a portable computer such as e.g. a smartphone or PDA. The movement repetitions are added, like in the invention U.S. Pat. No. 6,796,925B2. To this end, two different modules are necessary. Firstly, a “portable computer device”, such as e.g. a smartphone and an external transmitter with accelerometer, which transmits the measurement data wirelessly to the portable computer.
EP 1688746A2 describes an invention which measures human body movements. These body movements are acquired by means of an accelerometer.
WO 0169180A1 describes an invention, which renders it possible to measure the speed and distance during a running motion, for example during endurance training.
U.S. Pat. No. 6,820,025B2 describes an invention for identifying movement on a rigid body connected by hinges. This invention can determine the position of a sensor in space.
U.S. Ser. No. 00/580,7284A describes an invention for tracking the human head or bodies of similar size. By way of example, this invention serves to track head movements in virtual reality applications.
DE 10029459A1 describes an invention for acquiring the position and/or movement of an object and/or living being and parts of this apparatus. By way of example, this invention is suitable for determining the position of a match ball on an association football field in order, for example, to determine whether the ball was positioned behind the goal line in the case of a shot on goal.
DE 10029459A1 describes an invention which can recognize, track, display and identify the repeating movements of swimmers. The application of the invention relates to swimming-specific movement patterns, two movement axes and acceleration data.
CA 1148186 describes an invention, which enables tennis players to learn the controlled bending of the wrist. “It is therefore the primary object of this invention to provide means whereby a player can be automatically informed of errors, so that he can learn to avoid them.” (CA 1148186, pages 1-2). In order to determine the bend of the wrist, use is made of several bands and cables, and also an external recording device and sensor unit. The invention is not situated in a single closed device. The external recording device stores the number and frequency of the bends of the wrist.
DE 4222373A1 describes an invention for measuring path and speed of athletes such as e.g. skiers, surfers, sailors or cyclists. Use is made of an accelerometer for calculating the path and the speed.
DE 19830359A2 describes an invention for determining spatial positioning and movement of body parts and bodies by means of a combination of inertial orientation pickups and position acquisition sensor systems. By way of example, this invention could be used to determine the position of a body segment in space or in a partial coordinate system.
U.S. Ser. No. 00/567,6157A describes an invention for determining kinematically restricted multi-hinged structures. This invention renders it possible to determine the spatial position and orientation of body segments.
DE 102006047099A1 describes an invention for collecting and preparing training data in a fitness studio. This invention enables an acquisition of training data on stationary training equipment in the form of force, movement and repetition information and the preparation of the data for monitoring the training.
US 20070219059A1 describes an invention for continuously monitoring exercises and the real-time analysis thereof. As a result of this invention, it is possible to monitor body noises, body signs, vital functions, movements and machine settings continuously and automatically. This invention is designed specifically for heart-lung monitoring of an athlete during a training program in order to ensure the safety when carrying out exercises, particularly in the case of rehabilitation patients.
U.S. Pat. No. 4,660,829 describes an invention, which renders it possible to acquire movements of two body segments, e.g. the wrist and the forearm, in sports such as e.g. tennis. Two separate modules are used to acquire these movements.
US 20110082394A1 describes an invention for monitoring sports-related fitness by estimating the muscle strength and the common strength of extremities, said invention consisting of a sensor module and a force/path detection module for classifying movement series in relation to the muscle strength and the common strength of the limbs. By way of example, this invention can be used to identify/classify movements, which, for example, are carried out in the upper and lower limbs.
U.S. Pat. No. 6,514,219B1 describes an invention for automatic biomechanical analysis and identification and correction of posture deviations. By means of optical markers at various body joints, this invention renders it possible to detect said body joints in space and to undertake analyses.
U.S. Pat. No. 6,834,436B2 describes an invention in order to be able to distinguish a lying, seated or standing position of the human body. Furthermore, this invention can be used to determine too much or too little activity of joints or movements.
In order to analyze training data, use has until now been made in sports sciences, particularly in team sports and in endurance training, of mathematical and statistical models or unconventional modeling paradigms. By way of example, these models serve in predicting competition performance (e.g. in swimming) or in analyzing tactical interactions in team sports (e.g. in association football). Until now, previous models, which are intended to serve for analysis and predictions of training effects (performance), have a low model quality and prediction power, greatly simplify the interaction of training load and performance (e.g. one input variable and one output variable) or do not allow causal interpretations of the results. Furthermore, these are restricted in terms of their temporal depth, linked to a multiplicity of conditions (e.g. only advanced athletes) and the results are not evaluated by algorithm, i.e. they do not result in specific training recommendations. In order to deduce training recommendations from the results of such a model, there was always need for experts (e.g. trainers), who can interpret the difficult to understand connection between training load and performance.